OpenRaiser/Intern-Atlas huggingface dataset:
Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models
Venue: arXiv cs.CR 2025
Year: 2025

Maintaining security in IoT systems depends on intrusion detection since these networks' sensitivity to cyber-attacks is growing. Based on the IoT23 dataset, this study explores the use of several Machine Learning (ML) and Deep Learning (DL) along with the hybrid models for binary and multi-class intrusion detection. The standalone machine and deep learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN) were used. Furthermore, two hybrid models were created by combining machine learning techniques: RF, XGBoost, AdaBoost, KNN, and SVM and these hybrid models were voting based hybrid classifier. Where one is for binary, and the other one is for multi-class classification. These models vi were tested using precision, recall, accuracy, and F1-score criteria and compared the performance of each model. This work thoroughly explains how hybrid, standalone ML and DL techniques could improve IDS (Intrusion Detection System) in terms of accuracy and scalability in IoT (Internet of Things).
Provenance of Adaptation in Scientific and Business Workflows -- Literature Review
Venue: arXiv cs.SE 2025
Year: 2025

In the world of science new technology have opened up the possibility to rely on advanced computational methods and models to conduct and produce scientific research. An important aspect of scientific and business workflows is provenance - which refers to the information describing the production, history or lineage of an end product, which can also be data, digitalized processes and other not tangible artifacts. While there are already systems, tools and standards to capture provenance of data and workflows the provenance of adaptations/changes in workflows has not been addressed yet. In this paper we carry out a literature review to establish the state of the art on this topic and present our methodology and findings. Our findings confirm that provenance of adaptation has not been addressed adequately in the fields of business and scientific workflows. The two fields also have different motivation for recording the lineage of data or processes. While scientific workflows are interested in reproducibility and visualization, business workflows solutions are indirectly connected to compliance, exception handling and analysis. The adaptive nature of workflows in both fields is not reflected in the research on process provenance yet, as our results show. The use of standard provenance standards is also not wide spread.
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces TRIDIS (Tria Digita Scribunt), an open-source corpus of medieval and early modern manuscripts. TRIDIS aggregates multiple legacy collections (all published under open licenses) and incorporates large metadata descriptions. While prior publications referenced some portions of this corpus, here we provide a unified overview with a stronger focus on its constitution. We describe (i) the narrative, chronological, and editorial background of each major sub-corpus, (ii) its semi-diplomatic transcription rules (expansion, normalization, punctuation), (iii) a strategy for challenging out-of-domain test splits driven by outlier detection in a joint embedding space, and (iv) preliminary baseline experiments using TrOCR and MiniCPM2.5 comparing random and outlier-based test partitions. Overall, TRIDIS is designed to stimulate joint robust Handwritten Text Recognition (HTR) and Named Entity Recognition (NER) research across medieval and early modern textual heritage.
Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping
Venue: arXiv cs.LG 2025
Year: 2025

Reinforcement learning (RL) often struggles with reward misalignment, where agents optimize given rewards but fail to exhibit the desired behaviors. This arises when the reward function incentivizes proxy behaviors misaligned with the true objective. While human-in-the-loop (HITL) methods can mitigate this issue, they also introduce biases, leading to inconsistent and subjective feedback that complicates learning. To address these challenges, we propose two key contributions. First, we extend the use of zero-shot, off-the-shelf large language models (LLMs) for reward shaping beyond natural language processing (NLP) to continuous control tasks. Using LLMs as direct feedback providers eliminates the need for surrogate models trained on human feedback, which often inherit biases from training data. Second, we introduce a hybrid framework (LLM-HFBF) that enables LLMs to identify and correct biases in human feedback while incorporating this feedback into the reward shaping process. The LLM-HFBF framework creates a more balanced and reliable system by addressing both the limitations of LLMs (e.g., lack of domain-specific knowledge) and human supervision (e.g., inherent biases). By enabling human feedback bias flagging and correction, our approach improves reinforcement learning performance and reduces reliance on potentially biased human feedback. Empirical experiments show that biased human feedback significantly reduces performance, with Average Episodic Reward dropping by nearly 94% compared to unbiased approaches. In contrast, LLM-based methods sustain performance at a similar level to unbiased feedback, even in challenging edge-case scenarios.
MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
Venue: arXiv cs.LG 2025
Year: 2025

To ensure the trustworthiness and interpretability of AI systems, it is essential to align machine learning models with human domain knowledge. This can be a challenging and time-consuming endeavor that requires close communication between data scientists and domain experts. Recent leaps in the capabilities of Large Language Models (LLMs) can help alleviate this burden. In this paper, we propose a Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) which combines a data-driven black-box model with knowledge extracted from an LLM to enable domain knowledge-aligned and transparent predictions. While the introduced Mixture of Rule Experts (MoRE) steers the discovery of local rule-based surrogates during training and their utilization for the classification task, the LLM is responsible for enhancing the domain knowledge alignment of the rules by correcting and contextualizing them. Importantly, our method does not rely on access to the LLM during test time and ensures interpretability while not being prone to LLM-based confabulations. We evaluate our method on several tabular data sets and compare its performance with interpretable and non-interpretable baselines. Besides performance, we evaluate our grey-box method with respect to the utilization of interpretable rules. In addition to our quantitative evaluation, we shed light on how the LLM can provide additional context to strengthen the comprehensibility and trustworthiness of the model's reasoning process.
Concept Map Assessment Through Structure Classification
Venue: arXiv cs.CY 2025
Year: 2025

Due to their versatility, concept maps are used in various educational settings and serve as tools that enable educators to comprehend students' knowledge construction. An essential component for analyzing a concept map is its structure, which can be categorized into three distinct types: spoke, network, and chain. Understanding the predominant structure in a map offers insights into the student's depth of comprehension of the subject. Therefore, this study examined 317 distinct concept map structures, classifying them into one of the three types, and used statistical and descriptive information from the maps to train multiclass classification models. As a result, we achieved an 86\% accuracy in classification using a Decision Tree. This promising outcome can be employed in concept map assessment systems to provide real-time feedback to the student.
Advancing Spatiotemporal Prediction using Artificial Intelligence: Extending the Framework of Geographically and Temporally Weighted Neural Network (GTWNN) for Differing Geographical and Temporal Contexts
Venue: arXiv cs.LG 2025
Year: 2025

This paper aims at improving predictive crime models by extending the mathematical framework of Artificial Neural Networks (ANNs) tailored to general spatiotemporal problems and appropriately applying them. Recent advancements in the geospatial-temporal modelling field have focused on the inclusion of geographical weighting in their deep learning models to account for nonspatial stationarity, which is often apparent in spatial data. We formulate a novel semi-analytical approach to solving Geographically and Temporally Weighted Regression (GTWR), and applying it to London crime data. The results produce high-accuracy predictive evaluation scores that affirm the validity of the assumptions and approximations in the approach. This paper presents mathematical advances to the Geographically and Temporally Weighted Neural Network (GTWNN) framework, which offers a novel contribution to the field. Insights from past literature are harmoniously employed with the assumptions and approximations to generate three mathematical extensions to GTWNN's framework. Combinations of these extensions produce five novel ANNs, applied to the London and Detroit datasets. The results suggest that one of the extensions is redundant and is generally surpassed by another extension, which we term the history-dependent module. The remaining extensions form three novel ANN designs that pose potential GTWNN improvements. We evaluated the efficacy of various models in both the London and Detroit crime datasets, highlighting the importance of accounting for specific geographic and temporal characteristics when selecting modelling strategies to improve model suitability. In general, the proposed methods provide the foundations for a more context-aware, accurate, and robust ANN approach in spatio-temporal modelling.
Strategies for decentralised UAV-based collisions monitoring in rugby
Venue: arXiv cs.RO 2025
Year: 2025

Recent advancements in unmanned aerial vehicle (UAV) technology have opened new avenues for dynamic data collection in challenging environments, such as sports fields during fast-paced sports action. For the purposes of monitoring sport events for dangerous injuries, we envision a coordinated UAV fleet designed to capture high-quality, multi-view video footage of collision events in real-time. The extracted video data is crucial for analyzing athletes' motions and investigating the probability of sports-related traumatic brain injuries (TBI) during impacts. This research implemented a UAV fleet system on the NetLogo platform, utilizing custom collision detection algorithms to compare against traditional TV-coverage strategies. Our system supports decentralized data capture and autonomous processing, providing resilience in the rapidly evolving dynamics of sports collisions. The collaboration algorithm integrates both shared and local data to generate multi-step analyses aimed at determining the efficacy of custom methods in enhancing the accuracy of TBI prediction models. Missions are simulated in real-time within a two-dimensional model, focusing on the strategic capture of collision events that could lead to TBI, while considering operational constraints such as rapid UAV maneuvering and optimal positioning. Preliminary results from the NetLogo simulations suggest that custom collision detection methods offer superior performance over standard TV-coverage strategies by enabling more precise and timely data capture. This comparative analysis highlights the advantages of tailored algorithmic approaches in critical sports safety applications.
Harnessing uncertainty when learning through Equilibrium Propagation in neural networks
Venue: arXiv cs.LG 2025
Year: 2025

Equilibrium Propagation (EP) is a supervised learning algorithm that trains network parameters using local neuronal activity. This is in stark contrast to backpropagation, where updating the parameters of the network requires significant data shuffling. Avoiding data movement makes EP particularly compelling as a learning framework for energy-efficient training on neuromorphic systems. In this work, we assess the ability of EP to learn on hardware that contain physical uncertainties. This is particularly important for researchers concerned with hardware implementations of self-learning systems that utilize EP. Our results demonstrate that deep, multi-layer neural network architectures can be trained successfully using EP in the presence of finite uncertainties, up to a critical limit. This limit is independent of the training dataset, and can be scaled through sampling the network according to the central limit theorem. Additionally, we demonstrate improved model convergence and performance for finite levels of uncertainty on the MNIST, KMNIST and FashionMNIST datasets. Optimal performance is found for networks trained with uncertainties close to the critical limit. Our research supports future work to build self-learning hardware in situ with EP.
Learning to Reason for Long-Form Story Generation
Venue: arXiv cs.CL 2025
Year: 2025

Generating high-quality stories spanning thousands of tokens requires competency across a variety of skills, from tracking plot and character arcs to keeping a consistent and engaging style. Due to the difficulty of sourcing labeled datasets and precise quality measurements, most work using large language models (LLMs) for long-form story generation uses combinations of hand-designed prompting techniques to elicit author-like behavior. This is a manual process that is highly dependent on the specific story-generation task. Motivated by the recent success of applying RL with Verifiable Rewards to domains like math and coding, we propose a general story-generation task (Next-Chapter Prediction) and a reward formulation (Verified Rewards via Completion Likelihood Improvement) that allows us to use an unlabeled book dataset as a learning signal for reasoning. We learn to reason over a story's condensed information and generate a detailed plan for the next chapter. Our reasoning is evaluated via the chapters it helps a story-generator create, and compared against non-trained and supervised finetuning (SFT) baselines. Pairwise human judgments reveal the chapters our learned reasoning produces are preferred across almost all metrics, and the effect is more pronounced in Scifi and Fantasy genres.
Integer multiplication is at least as hard as matrix transposition
Venue: arXiv cs.CC 2025
Year: 2025

Working in the multitape Turing model, we show how to reduce the problem of matrix transposition to the problem of integer multiplication. If transposing an $n \times n$ binary matrix requires $Ω(n^2 \log n)$ steps on a Turing machine, then our reduction implies that multiplying $n$-bit integers requires $Ω(n \log n)$ steps. In other words, if matrix transposition is as hard as expected, then integer multiplication is also as hard as expected.
Predictive Traffic Rule Compliance using Reinforcement Learning
Venue: arXiv cs.RO 2025
Year: 2025

Autonomous vehicle path planning has reached a stage where safety and regulatory compliance are crucial. This paper presents an approach that integrates a motion planner with a deep reinforcement learning model to predict potential traffic rule violations. Our main innovation is replacing the standard actor network in an actor-critic method with a motion planning module, which ensures both stable and interpretable trajectory generation. In this setup, we use traffic rule robustness as the reward to train a reinforcement learning agent's critic, and the output of the critic is directly used as the cost function of the motion planner, which guides the choices of the trajectory. We incorporate some key interstate rules from the German Road Traffic Regulation into a rule book and use a graph-based state representation to handle complex traffic information. Experiments on an open German highway dataset show that the model can predict and prevent traffic rule violations beyond the planning horizon, increasing safety and rule compliance in challenging traffic scenarios.
Plug & Offload: Transparently Offloading TCP Stack onto Off-path SmartNIC with PnO-TCP
Venue: arXiv cs.DC 2025
Year: 2025

Host CPU resources are heavily consumed by TCP stack processing, limiting scalability in data centers. Existing offload methods typically address only partial functionality or lack flexibility. This paper introduces PnO (Plug & Offload), an approach to fully offload TCP processing transparently onto off-path SmartNICs (NVIDIA BlueField DPUs). Key to our solution is PnO-TCP, a novel TCP stack specifically designed for efficient execution on the DPU's general-purpose cores, panning both the host and the SmartNIC to facilitate the offload. PnO-TCP leverages a lightweight, user-space stack based on DPDK, achieving high performance despite the relatively modest computational power of off-path SmartNIC cores. Our evaluation, using real-world applications (Redis, Lighttpd, and HAProxy), demonstrates that PnO achieves transparent TCP stack offloading, leading to both substantial reductions in host CPU usage and, in many cases, significant performance improvements, particularly for small packet scenarios (< 2KB) where RPS gains of 34%-127% were observed in single-threaded tests. Our evaluation, using real-world applications (Redis, Lighttpd, and HAProxy), demonstrates that PnO achieves transparent TCP stack offloading, leading to both substantial reductions in host CPU usage and, in many cases, significant performance improvements, particularly for small packet scenarios (< 2KB) where RPS gains of 34%-127% were observed in single-threaded tests.
Bi-Level Multi-View fuzzy Clustering with Exponential Distance
Venue: arXiv cs.CV 2025
Year: 2025

In this study, we propose extension of fuzzy c-means (FCM) clustering in multi-view environments. First, we introduce an exponential multi-view FCM (E-MVFCM). E-MVFCM is a centralized MVC with consideration to heat-kernel coefficients (H-KC) and weight factors. Secondly, we propose an exponential bi-level multi-view fuzzy c-means clustering (EB-MVFCM). Different to E-MVFCM, EB-MVFCM does automatic computation of feature and weight factors simultaneously. Like E-MVFCM, EB-MVFCM present explicit forms of the H-KC to simplify the generation of the heat-kernel $\mathcal{K}(t)$ in powers of the proper time $t$ during the clustering process. All the features used in this study, including tools and functions of proposed algorithms will be made available at https://www.github.com/KristinaP09/EB-MVFCM.
Fast and Accurate Silent Vulnerability Fix Retrieval
Venue: arXiv cs.CR 2025
Year: 2025

An upstream task for software bill-of-materials (SBOMs) is the accurate localization of the patch that fixes a vulnerability. Nevertheless, existing work reveals a significant gap in the CVEs whose patches exist but are not traceable. Existing works have proposed several approaches to trace/retrieve the patching commit for fixing a CVE. However, they suffer from two major challenges: (1) They cannot effectively handle long diff code of a commit; (2) We are not aware of existing work that scales to the full repository with satisfactory accuracy. Upon identifying this gap, we propose SITPatchTracer, a scalable and effective retrieval system for tracing known vulnerability patches. To handle the context length challenge, SITPatchTracer proposes a novel hierarchical embedding technique which efficiently extends the context coverage to 6x that of existing work while covering all files in the commit. To handle the scalability challenge, SITPatchTracer utilizes a three-phase framework, balancing the effectiveness/efficiency in each phase. The evaluation of SITPatchTracer demonstrates it outperforms existing patch tracing methods (PatchFinder, PatchScout, VFCFinder) by a large margin. Furthermore, SITPatchTracer outperforms VoyageAI, the SOTA commercial code embedding LLM (\$1.8 per 10K commits) on the MRR and Recall@10 by 18\% and 28\% on our two datasets. Using SITPatchTracer, we have successfully traced and merged the patch links for 35 new CVEs in the GitHub Advisory database Our ablation study reveals that hierarchical embedding is a practically effective way of handling long context for patch retrieval.
Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data
Venue: arXiv cs.LG 2025
Year: 2025

The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. The proposed approach combines differential gene expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle and uses trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and exhibits low experimental variability in comparison to related deep learning-based models. The biomarkers identified by MOGKAN were validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates robust predictive performance and interpretability with potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.
Pallet Detection And Localisation From Synthetic Data
Venue: arXiv cs.CV 2025
Year: 2025

The global warehousing industry is experiencing rapid growth, with the market size projected to grow at an annual rate of 8.1% from 2024 to 2030 [Grand View Research, 2021]. This expansion has led to a surge in demand for efficient pallet detection and localisation systems. While automation can significantly streamline warehouse operations, the development of such systems often requires extensive manual data annotation, with an average of 35 seconds per image, for a typical computer vision project. This paper presents a novel approach to enhance pallet detection and localisation using purely synthetic data and geometric features derived from their side faces. By implementing a domain randomisation engine in Unity, the need for time-consuming manual annotation is eliminated while achieving high-performance results. The proposed method demonstrates a pallet detection performance of 0.995 mAP50 for single pallets on a real-world dataset. Additionally, an average position accuracy of less than 4.2 cm and an average rotation accuracy of 8.2° were achieved for pallets within a 5-meter range, with the pallet positioned head-on.